The subject matter disclosed herein relates to power distribution and, more particularly, to a system and method for time based or dynamic load profiling and forecasting by predicting and using where dynamic or moving objects will be in a distributed power network.
Traditional power distribution requires a power distributor to know the distribution of assets that require power for given locations (e.g., power grids) based on the volume of assets and a prediction of when power is to be needed (e.g., peak power times). With the advent of plug-in electric vehicles (PEV), such distributed assets are no longer static. Charging stations for PEVs are static locations, but it can be difficult to predict the volume of PEVs and the times of day that a particular charging station will service, which causes a shift of dynamic load that is not accurately forecasted with conventional monitoring technology. There exists no systems that can spatially determine where a particular distributed asset (e.g., a single PEV) will be located at a given time, what kind of load it will require (e.g., a length of charging time) and how it will connect to the network. As more dynamic assets are adopted by consumers and industry, the combined effects will have a great impact on specific areas where concentration is highest at given times of the day. These areas of higher concentration will lead to greater swings in the balance of overall grid stability and power distribution.